摘要
With the large-scale integration of renewable generation,energy storage system(ESS)is increasingly regarded as a promising technology to provide sufficient flexibility for the safe and stable operation of power systems under uncertainty.This paper focuses on grid-scale ESS planning problems in transmission-constrained power systems considering uncertainties of wind power and load.A scenario-based chance-constrained ESS planning approach is proposed to address the joint planning of multiple technologies of ESS.Specifically,the chance constraints on wind curtailment are designed to ensure a certain level of wind power utilization for each wind farm in planning decision-making.Then,an easy-to-implement variant of Benders decomposition(BD)algorithm is developed to solve the resulting mixed-integer nonlinear programming problem.Our case studies on an IEEE test system indicate that the proposed approach can co-optimize multiple types of ESSs and provide flexible planning schemes to achieve the economic utilization of wind power.In addition,the proposed BD algorithm can improve the computational efficiency in solving this kind of chance-constrained problems.
With the large-scale integration of renewable generation,energy storage system(ESS) is increasingly regarded as a promising technology to provide sufficient flexibility for the safe and stable operation of power systems under uncertainty.This paper focuses on grid-scale ESS planning problems in transmission-constrained power systems considering uncertainties of wind power and load.A scenario-based chance-constrained ESS planning approach is proposed to address the joint planning of multiple technologies of ESS.Specifically,the chance constraints on wind curtailment are designed to ensure a certain level of wind power utilization for each wind farm in planning decision-making.Then,an easy-to-implement variant of Benders decomposition(BD) algorithm is developed to solve the resulting mixed-integer nonlinear programming problem.Our case studies on an IEEE test system indicate that the proposed approach can co-optimize multiple types of ESSs and provide flexible planning schemes to achieve the economic utilization of wind power.In addition,the proposed BD algorithm can improve the computational efficiency in solving this kind of chance-constrained problems.
基金
supported by National Key Research and Development Program of China(No.2017YFB0902200)
the Science and Technology Project of State Grid Corporation of China(No.5228001700CW).
作者简介
Jianxue WANG received the B.S.,M.S.,and Ph.D.degrees in electrical engineering from Xi’an Jiaotong University,China,in 1999,2002,and 2006,respectively.He is currently a Professor in the School of Electrical Engineering,Xi’an Jiaotong University.His current research interests include microgrid planning and schdeuling,power system planning and scheduling,and electricity market.jxwang@mail.xjtu.edu.cn;Yunhao LI received the B.S.degree in electrical engineering from Huazhong University of Science and Technology,Wuhan,China,in 2012.He is currently pursuing the Ph.D.degree at Xi’an Jiaotong University,Xi’an,China.His research interests include renewable energy and power system planning.yhli_ee@126.com;Chenjia GU received the B.S.degree from the School of Electrical Engineering,Xi’an Jiaotong University,China,in 2017.He is currently working toward the Ph.D.degree at Xi’an Jiaotong University.His major research interests include power system optimization and renewable energy/energy storage integration.809679595@qq.com;Jinshan LIU received the B.S.degree from Shenyang University of Technology,Shenyang,China,and the M.S.degree from Northeast Electric Power University,Jilin,China.He has been engaged inresearch work in power system operation and control since joining the work.js-clark@163.com;Zhengxi LI received the B.S.degree from Shanghai Jiao Tong University,Shanghai,China,and the M.S.degree from Chongqing University,Chongqing,China.He has been engaged in research work in photovoltaic field since joining the work.18209783306@163.com
